Read counts summary

1 Requirements

• loading libraries dplyr, ggplot2, tidyr, Hmisc

2 Sample overview

2.1 Data import

• Checking if result file(s) are available:

• Importing result table(s):

[1] "Import of counts table complete."

2.2 Sample table

• overview of samples

3 Quality control

3.1 Total number of mapped reads per sample

• figure shows the total number of mapped reads used for fitness score calculation
• all other reads that were filtered out during preprocessing (low quality, low mapping score) are not included

3.2 Number of individual barcodes per sample

• figure shows the number of individual, unique barcodes per sample
• depnding on bowtie settings, a certain number of mismatches in barcodes are allowed

3.3 Number of missing barcodes per sample

• figure shows the number of missing barcodes per sample
• this number is determined from the number of total encountered barcodes across all samples

3.4 Number of barcodes per gene, per sample

• figure shows the number of barcodes per gene, per sample
• the x-axis shows the number of genes with N barcodes, broken down by sample
• barcodes without mapped reads for the respective sample are removed
• for example a colored bar with label ≤ 2 shows number of genes with less or equal than 2 barcodes

3.5 Read count distribution, violin plot

• figure shows the read count distribution per sample and barcode
• read count per barcode is only shown for the first 1000 barcodes to reduce processing time
• barcodes without mapped reads for the respective sample are removed
• read count is log 10 transformed (0 -> 1, 1 -> 10, 2 -> 100, …)

3.6 Read count distribution, histogram

• figure shows the same data as above, but with full set of barcodes per sample
• barcodes without mapped reads for the respective sample are removed
• read count is log 10 transformed (0 -> 1, 1 -> 10, 2 -> 100, …)

3.7 Top 10 most abundant barcodes, per sample

• figure shows top 10 barcodes ranked by read count
• ideally, initial time point samples show high equality of barcode abundance
• extremely high abundance of very few barcodes is a sign of few mutants outcompeting the population
• this can happen when libraries are (pre-) cultivated for too long periods of time
• can lead to downstream problems as it reduces library diversity (depletes low abundant mutants)

3.8 Cumulative read count distribution and barcode diversity

• figure shows the barcode diversity by plotting fraction of reads (%) vs fraction of barcodes (%)
• the ideal library has high diversity and equal distribution of barcodes for initial time points
• such a distribution would follow the diagonal dashed grey line
• if reads per barcode (red line) are not well distributed, % of reads (y-axis) shows a steep ascent
• this means very few barcodes contribute to almost all reads

• this table shows the area under curve (AUC) for the line plot above
• an AUC of 0.5 is ideal, an AUC approaching 1.0 is not optimal
• the ‘Gini index’ is a score between 0 and 1 measuring population equality
• it is defined as the (AUC - AUC_optimal) / AUC_optimal
• a Gini score of 0 describes a perfectly equal, a Gini score of 1.0 a perfectly unequal distribution

3.9 Sample and replicate correlation coefficent (R)

• figure shows heat map with correlation coefficient R (-1 < R < 1)
• correlation coefficient shows how strongly read abundance is correlated

3.10 Sample and replicate similarity with PCA

• this figure shows sample similarity similar to above figure
• uses principal component analysis (PCA) to reduce the multidimensional data to 3 main dimensions
• plotted are principal component 1 (x-axis), 2 (y-axis) and 3 (point size)
• replicates for same samples should cluster together
• outliers should be easily visible

4 Report info

The template for this report is located in ./nf-core-crispriscreen/bin/counts_summary.Rmd.

Date: 2024-04-10

Author: Michael Jahn, PhD

Affilations:

• Science For Life Laboratory (KTH), Stockholm, Sweden
• Max-Planck-Unit for the Science of Pathogens, Berlin, Germany

5 Session Info

R version 4.3.3 (2024-02-29)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS

Matrix products: default
BLAS/LAPACK: /conda-env/fitness/lib/libopenblasp-r0.3.27.so;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

time zone: NA
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] Hmisc_5.1-2   tidyr_1.3.1   ggplot2_3.5.0 dplyr_1.1.4  

loaded via a namespace (and not attached):
 [1] sass_0.4.9        utf8_1.2.4        generics_0.1.3    stringi_1.8.3    
 [5] digest_0.6.35     magrittr_2.0.3    evaluate_0.23     grid_4.3.3       
 [9] fastmap_1.1.1     jsonlite_1.8.8    nnet_7.3-19       backports_1.4.1  
[13] Formula_1.2-5     gridExtra_2.3     purrr_1.0.2       fansi_1.0.6      
[17] scales_1.3.0      jquerylib_0.1.4   cli_3.6.2         rlang_1.1.3      
[21] munsell_0.5.1     base64enc_0.1-3   withr_3.0.0       cachem_1.0.8     
[25] yaml_2.3.8        tools_4.3.3       checkmate_2.3.0   htmlTable_2.4.2  
[29] colorspace_2.1-0  vctrs_0.6.5       R6_2.5.1          rpart_4.1.23     
[33] lifecycle_1.0.4   stringr_1.5.1     htmlwidgets_1.6.4 foreign_0.8-86   
[37] cluster_2.1.6     pkgconfig_2.0.3   pillar_1.9.0      bslib_0.7.0      
[41] gtable_0.3.4      glue_1.7.0        data.table_1.15.2 highr_0.10       
[45] xfun_0.43         tibble_3.2.1      tidyselect_1.2.0  rstudioapi_0.16.0
[49] knitr_1.46        farver_2.1.1      htmltools_0.5.8.1 rmarkdown_2.26   
[53] labeling_0.4.3    compiler_4.3.3